1. Lipinski’s rule of yuppieraj@gmail.com +91-9949611237

2. 2 Correlating physical properties of drug with oral bioavailabi lity

3. Selected parameters for testing Molecular weight Drug molecular weight is inversely proportinal to drug permeability Lipophilicity Log P Drug Lipophilicity is directly proportinal to drug permeability Number of hydrogen bond donors and acceptors Hydrogen bond donors and acceptors inversely proportinal to drug permeability

4. Lipinski’s or Pfizer's rule of five There are more than H-bond donors. The molecular weight is over Daltons. (For a linear molecule) The LogP is over (or MLOGP is over 4.15) There are more than H-bond acceptors. Poor permeation/ Poor absorption happens when 5

5. Remarks: No more than one violation; not applicable for substrates of transporters and natural products Extensions:

6. Partition coefficient definition The ratio of the equilibrium concentrations of a dissolved substance in a two-phase system containing two largely immiscible solvents (water and n- octanol) 1-octanol Water

7. Partition coefficient (cont.) Since the differences are usually on a very large scale, Log 10 (P) is used. OH 1-Octanol O HH Water

8. Definition of logP—the octanol-water partition coefficient, P, is a measure of the differential solubility of a neutral substance between these immiscible liquids and thereby, a descriptor of hydrophobicity (or the lipophilicity) of a neutral substance. It is typically used in its logarithmic form, logP.

9. MLogP – Moriguchi’s correction Problem – A straightforward counting of lipophilic atoms and hydrophilic atoms account for only 73% of the variance in the experimental LogP. Therefore, corrections should be applied

10. Exception to the rule of five Compound classes that are substrates for biological transporters: Antibiotics Fungicides-Protozoacides -antiseptics Vitamins Cardiac glycosides. Log P is not an accurate determinant of lipophilicity for ionizable compounds because it only correctly describes the partition coefficient of neutral (uncharged) molecules. The distribution coefficient (Log D) is the correct descriptor for ionizable systems.lipophilicity ionizabledistribution coefficient

11. limitations the rule does not predict if a compound is pharmacologically active

12. Variants In an attempt to improve the predictions of druglikeness, the rules have spawned many extensions, for example the following: [6] druglikeness [6] Partition coefficient log P in −0.4 to +5.6 range Partition coefficient Molar refractivity from 40 to 130 Molar refractivity Molecular weight from 180 to 500 Number of atoms from 20 to 70 (includes H- bond donors [e.g.;OH's and NH's] and H-bond acceptors [e.g.; N's and O's]) Polar surface area no greater than 140 Ǻ 2

13. molecular massmolecular mass less than 300 daltons daltons

14. Conclusions The majority of drugs are intended for oral therapy, which is not predictable. The in-vitro nature of HTS techniques shifts leads toward lower solubility. Therefore – obtaining oral activity may be the rate limiting step. Computational methods in the early discovery setting may use as a filter that shifts SAR toward compounds with greater probability for oral activity

15. Subsequently, several other rules have been proposed, for example Veber et al. [9] identified that most of the 1100 compounds they studied with oral bioavailability of greater than 20% in rats had less than 10 rotatable bonds and a Polar Surface Area (PSA) of less than 140 Å2. However, Lu et al. [10] repeated this study with a set of 434 compounds and showed that the criteria depended on the method used for calculation, providing one illustration of the need for flexibility in the criteria depending on the source of data.

16. Johnson et al. identified rules based on MW and the logarithm of the octanol:buffer partition coefficient at pH7.4 (logD) to achieve permeability and metabolic stability [11]. In this case, rather than expressing these rules as criteria for the individual characteristics, Johnson et al. identified correlations that led them to express the rules in terms of a ‘golden triangle’ that defines an optimal region in (MW,logD) space in which a compound should lie

17. Other rules, involving parameters such as the fraction of carbons which are sp3 hybridized [12] and the number of aromatic rings [13] have been proposed as measures of developability or likelihood of clinical success. Furthermore, Hughes et al. [14] studied the relationship between physicochemical properties and adverse events observed in in vivo toleration studies. They concluded that compounds with both calculated logP (clogP) > 3 and topological polar surface area (TPSA) < 75 Å2 had a significantly increased safety risk. The undoubted popularity of these rules derives from their simplicity and interpretability, the first requirement for a good MPO method. It is very easy to calculate these characteristics and quickly check if a compound obeys these rules. Similarly it is easy to understand how to modify a compound that fails to meet these rules in order to improve its chance of success; it is clear how MW, HBD or HBA could be reduced and

18. Table 2. An example of a target product profile for selection of a lead compound intended for oral administration. Property Criterion Pharmacology Potency against target (Ki) <100 nM Selectivity against related off-targets >100 X Physicochemical LogP <4 Solubility >100 microM MW <450 Da ADME Caco-2* permeability (P app ) >10X10 -6 cm/s Intrinsic Clearance in Human Liver Microsomes (Cl int ) <25 microL/min/mg protein Absence of P-glycoprotein transport (Caco2 BA:AB) <3 Safety Avoid Cytochrome P450-mediated drug-drug interactions (K i for CYP3A4, CYP2C9, CYP2D6, CYP1A2) >1 microM Avoid interaction with hERG potassium ion channel (IC 50 ) >10 microM Cytotoxicity in HepG2† cells (LD50) >1 microM

19. Conclusions (cont) Calculations, however imprecise (give only probabilities), may help when choices must be made as to the design or purchase Accurate prediction of solubility of complex compound is still an “elusive target”

20. Acknowledgement Christopher A. Lipinski Pfizer Global R&D, Groton Labs, Eastern Point Road, MS 8200-36, Groton, CT 06340, USA

23. Example: Aciclovir H-donors : 4 MW: 225.21 MLOGP: -0.09 (exp. LogP: -1.56) H-acceptors : 8 Aciclovir would pass the Rule of 5

24. References to slides prior to conclusion 9 Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002;45:2615-2623. 10 Lu JJ, Crimin K, Goodwin JT, Crivori P, Orrenius C, Xing L, Tandler PJlVTJ, Amore BM, Wilson AGE, Stouten PFW, et al. Influence of Molecular Flexibility and Polar Surface Area Metrics on Oral Bioavailability in the Rat. J. Med. Chem. 2004;47:6104-6107. 11 Johnson TW, Dress KR, Edwards M. Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 2009;19:5560-5564. 12 Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 2009;52:6752-6756. 13 Ritchie TJ, Macdonald SJF. The impact of aromatic ring count on compound developability – are too many aromatic rings a liability in drug design? Drug Discov. Today. 2009;14:1011- 1020. 14 Hughes JD, Blagg J, Price DA, Bailey S, DeCrescenzo GA, Devraj RV, Ellsworth E, Fobian YM, Gibbs ME, Gilles RW, et al. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 2008;18:4872-4875.

25. References Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (March 2001). "Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings". Adv. Drug Deliv. Rev. 46 (1-3): 3–26. Lipinski CA (December 2004). "Lead- and drug-like compounds: the rule-of-five revolution". Drug Discovery Today: Technologies 1 (4): 337–341 Oprea TI, Davis AM, Teague SJ, Leeson PD (2001). "Is there a difference between leads and drugs? A historical perspective". J Chem Inf Comput Sci 41 (5): 1308–15 Leo A, Hansch C, Elkins D (1971). "Partition coefficients and their uses". Chem Rev 71 (6): 525–616 C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Adv. Drug Deliv. Rev. 1997, 23, 3-25.

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